Overview

Dataset statistics

Number of variables44
Number of observations798
Missing cells2419
Missing cells (%)6.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory636.4 KiB
Average record size in memory816.6 B

Variable types

Text1
Categorical27
Numeric16

Alerts

warranty is highly imbalanced (63.7%)Imbalance
touch_screen is highly imbalanced (58.8%)Imbalance
hdmi is highly imbalanced (68.7%)Imbalance
display_port is highly imbalanced (86.5%)Imbalance
vga is highly imbalanced (97.5%)Imbalance
inbuilt_microphone is highly imbalanced (90.3%)Imbalance
processor_brand is highly imbalanced (53.8%)Imbalance
graphics_model is highly imbalanced (51.1%)Imbalance
business is highly imbalanced (62.0%)Imbalance
weight has 67 (8.4%) missing valuesMissing
threads has 21 (2.6%) missing valuesMissing
battery_capacity has 202 (25.3%) missing valuesMissing
battery_cell has 159 (19.9%) missing valuesMissing
usb2 has 561 (70.3%) missing valuesMissing
usb3 has 50 (6.3%) missing valuesMissing
typec has 47 (5.9%) missing valuesMissing
processor_gen has 43 (5.4%) missing valuesMissing
graphics_capacity has 478 (59.9%) missing valuesMissing
hdd has 779 (97.6%) missing valuesMissing
laptop_model has unique valuesUnique
thickness has 185 (23.2%) zerosZeros
ssd has 19 (2.4%) zerosZeros

Reproduction

Analysis started2024-04-30 21:30:14.299632
Analysis finished2024-04-30 21:31:00.276580
Duration45.98 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

laptop_model
Text

UNIQUE 

Distinct798
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size118.3 KiB
2024-05-01T03:01:00.528764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length127
Median length99
Mean length86.833333
Min length37

Characters and Unicode

Total characters69293
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique798 ?
Unique (%)100.0%

Sample

1st rowAcer One 14 Z8-415 Laptop (11th Gen Core i3 / 8GB/ 512GB SSD/ Win11 Home)
2nd rowWings Nuvobook V1 Laptop (11th Gen Core i5/ 8GB/ 512GB SSD/ Win11)
3rd rowMSI Thin GF63 12HW-012IN Gaming Laptop (12th Gen Core i5/ 16GB/ 512GB SSD/ Win11 Home/ 4GB Graphics)
4th rowAcer Nitro V ANV15-51 Gaming Laptop (13th Gen Core i5/ 8GB/ 512GB SSD/ Win11/ 6GB Graph)
5th rowAcer Aspire Lite AL15-51 Laptop (AMD Ryzen 5 5500U/ 16GB/ 512GB SSD/ Win11)
ValueCountFrequency (%)
laptop 790
 
6.6%
ssd 764
 
6.4%
win11 665
 
5.6%
core 557
 
4.7%
gen 555
 
4.6%
512gb 525
 
4.4%
16gb 416
 
3.5%
home 379
 
3.2%
8gb 348
 
2.9%
graph 285
 
2.4%
Other values (1095) 6680
55.8%
2024-05-01T03:01:01.084446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11166
 
16.1%
1 4454
 
6.4%
G 3145
 
4.5%
o 2944
 
4.2%
/ 2710
 
3.9%
e 2483
 
3.6%
n 2303
 
3.3%
i 2197
 
3.2%
B 2112
 
3.0%
S 2091
 
3.0%
Other values (58) 33688
48.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21784
31.4%
Uppercase Letter 17432
25.2%
Decimal Number 14021
20.2%
Space Separator 11166
16.1%
Other Punctuation 2792
 
4.0%
Close Punctuation 799
 
1.2%
Open Punctuation 798
 
1.2%
Dash Punctuation 501
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 3145
18.0%
B 2112
12.1%
S 2091
12.0%
D 1139
 
6.5%
L 1096
 
6.3%
W 971
 
5.6%
A 824
 
4.7%
H 774
 
4.4%
C 748
 
4.3%
I 539
 
3.1%
Other values (16) 3993
22.9%
Lowercase Letter
ValueCountFrequency (%)
o 2944
13.5%
e 2483
11.4%
n 2303
10.6%
i 2197
10.1%
p 2053
9.4%
a 1780
8.2%
t 1566
7.2%
r 1337
 
6.1%
h 974
 
4.5%
m 749
 
3.4%
Other values (16) 3398
15.6%
Decimal Number
ValueCountFrequency (%)
1 4454
31.8%
5 2046
14.6%
2 1818
13.0%
0 1466
 
10.5%
3 1089
 
7.8%
6 996
 
7.1%
4 701
 
5.0%
8 641
 
4.6%
7 602
 
4.3%
9 208
 
1.5%
Other Punctuation
ValueCountFrequency (%)
/ 2710
97.1%
. 82
 
2.9%
Space Separator
ValueCountFrequency (%)
11166
100.0%
Close Punctuation
ValueCountFrequency (%)
) 799
100.0%
Open Punctuation
ValueCountFrequency (%)
( 798
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 501
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39216
56.6%
Common 30077
43.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 3145
 
8.0%
o 2944
 
7.5%
e 2483
 
6.3%
n 2303
 
5.9%
i 2197
 
5.6%
B 2112
 
5.4%
S 2091
 
5.3%
p 2053
 
5.2%
a 1780
 
4.5%
t 1566
 
4.0%
Other values (42) 16542
42.2%
Common
ValueCountFrequency (%)
11166
37.1%
1 4454
 
14.8%
/ 2710
 
9.0%
5 2046
 
6.8%
2 1818
 
6.0%
0 1466
 
4.9%
3 1089
 
3.6%
6 996
 
3.3%
) 799
 
2.7%
( 798
 
2.7%
Other values (6) 2735
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11166
 
16.1%
1 4454
 
6.4%
G 3145
 
4.5%
o 2944
 
4.2%
/ 2710
 
3.9%
e 2483
 
3.6%
n 2303
 
3.3%
i 2197
 
3.2%
B 2112
 
3.0%
S 2091
 
3.0%
Other values (58) 33688
48.6%

brand
Categorical

Distinct27
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
Asus
164 
HP
156 
Lenovo
138 
MSI
88 
Dell
86 
Other values (22)
166 

Length

Max length9
Median length8
Mean length4.0601504
Min length2

Characters and Unicode

Total characters3240
Distinct characters41
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st rowAcer
2nd rowWings
3rd rowMSI
4th rowAcer
5th rowAcer

Common Values

ValueCountFrequency (%)
Asus 164
20.6%
HP 156
19.5%
Lenovo 138
17.3%
MSI 88
11.0%
Dell 86
10.8%
Acer 73
9.1%
Infinix 17
 
2.1%
Samsung 11
 
1.4%
Apple 10
 
1.3%
LG 10
 
1.3%
Other values (17) 45
 
5.6%

Length

2024-05-01T03:01:01.458583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
asus 165
20.7%
hp 156
19.5%
lenovo 138
17.3%
msi 88
11.0%
dell 86
10.8%
acer 73
9.1%
infinix 17
 
2.1%
samsung 11
 
1.4%
apple 10
 
1.3%
lg 10
 
1.3%
Other values (16) 44
 
5.5%

Most occurring characters

ValueCountFrequency (%)
s 351
 
10.8%
e 326
 
10.1%
o 293
 
9.0%
A 252
 
7.8%
u 193
 
6.0%
n 192
 
5.9%
l 187
 
5.8%
H 162
 
5.0%
P 158
 
4.9%
L 150
 
4.6%
Other values (31) 976
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2093
64.6%
Uppercase Letter 1147
35.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 351
16.8%
e 326
15.6%
o 293
14.0%
u 193
9.2%
n 192
9.2%
l 187
8.9%
v 140
 
6.7%
r 82
 
3.9%
c 73
 
3.5%
i 72
 
3.4%
Other values (14) 184
8.8%
Uppercase Letter
ValueCountFrequency (%)
A 252
22.0%
H 162
14.1%
P 158
13.8%
L 150
13.1%
I 105
9.2%
S 101
8.8%
M 88
 
7.7%
D 86
 
7.5%
G 18
 
1.6%
X 6
 
0.5%
Other values (7) 21
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 3240
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 351
 
10.8%
e 326
 
10.1%
o 293
 
9.0%
A 252
 
7.8%
u 193
 
6.0%
n 192
 
5.9%
l 187
 
5.8%
H 162
 
5.0%
P 158
 
4.9%
L 150
 
4.6%
Other values (31) 976
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 351
 
10.8%
e 326
 
10.1%
o 293
 
9.0%
A 252
 
7.8%
u 193
 
6.0%
n 192
 
5.9%
l 187
 
5.8%
H 162
 
5.0%
P 158
 
4.9%
L 150
 
4.6%
Other values (31) 976
30.1%

price
Real number (ℝ)

Distinct395
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85206.959
Minimum11990
Maximum569990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:01.646125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11990
5-th percentile26990
Q147990
median65745
Q394990
95-th percentile210171.35
Maximum569990
Range558000
Interquartile range (IQR)47000

Descriptive statistics

Standard deviation66340.745
Coefficient of variation (CV)0.77858366
Kurtosis10.531844
Mean85206.959
Median Absolute Deviation (MAD)22020
Skewness2.8190422
Sum67995153
Variance4.4010945 × 109
MonotonicityNot monotonic
2024-05-01T03:01:01.818006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59990 17
 
2.1%
54990 14
 
1.8%
62990 14
 
1.8%
49990 12
 
1.5%
69990 12
 
1.5%
58990 11
 
1.4%
36990 11
 
1.4%
109990 11
 
1.4%
79990 11
 
1.4%
44990 11
 
1.4%
Other values (385) 674
84.5%
ValueCountFrequency (%)
11990 2
0.3%
12989 1
 
0.1%
12990 1
 
0.1%
13990 1
 
0.1%
15990 2
0.3%
16990 4
0.5%
18990 3
0.4%
19990 1
 
0.1%
20890 1
 
0.1%
20990 2
0.3%
ValueCountFrequency (%)
569990 1
0.1%
453490 1
0.1%
446390 1
0.1%
429990 1
0.1%
415000 1
0.1%
399999 1
0.1%
390914 1
0.1%
379990 1
0.1%
367590 1
0.1%
354490 1
0.1%

num_votes
Real number (ℝ)

Distinct242
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.86717
Minimum51
Maximum14917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:02.034968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile56
Q174
median94
Q3122
95-th percentile857.65
Maximum14917
Range14866
Interquartile range (IQR)48

Descriptive statistics

Standard deviation1086.8141
Coefficient of variation (CV)3.8151609
Kurtosis130.38878
Mean284.86717
Median Absolute Deviation (MAD)22
Skewness10.772538
Sum227324
Variance1181164.9
MonotonicityNot monotonic
2024-05-01T03:01:02.232633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 18
 
2.3%
83 16
 
2.0%
110 15
 
1.9%
101 15
 
1.9%
106 15
 
1.9%
85 14
 
1.8%
58 14
 
1.8%
69 14
 
1.8%
94 14
 
1.8%
81 13
 
1.6%
Other values (232) 650
81.5%
ValueCountFrequency (%)
51 8
1.0%
52 7
0.9%
53 7
0.9%
54 8
1.0%
55 8
1.0%
56 11
1.4%
57 6
0.8%
58 14
1.8%
59 10
1.3%
60 10
1.3%
ValueCountFrequency (%)
14917 1
0.1%
14846 1
0.1%
13399 1
0.1%
12724 1
0.1%
6907 1
0.1%
4833 1
0.1%
4046 1
0.1%
3129 1
0.1%
3050 1
0.1%
2964 1
0.1%

ratings
Real number (ℝ)

Distinct24
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3323308
Minimum3.55
Maximum4.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:02.404557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.55
5-th percentile4
Q14.15
median4.3
Q34.5
95-th percentile4.7
Maximum4.75
Range1.2
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.23033951
Coefficient of variation (CV)0.053167573
Kurtosis-0.67660852
Mean4.3323308
Median Absolute Deviation (MAD)0.2
Skewness0.06418695
Sum3457.2
Variance0.053056292
MonotonicityNot monotonic
2024-05-01T03:01:02.576434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4.3 75
 
9.4%
4.2 72
 
9.0%
4.1 63
 
7.9%
4.15 57
 
7.1%
4.4 54
 
6.8%
4.25 51
 
6.4%
4.35 47
 
5.9%
4 46
 
5.8%
4.6 45
 
5.6%
4.5 42
 
5.3%
Other values (14) 246
30.8%
ValueCountFrequency (%)
3.55 1
 
0.1%
3.65 2
 
0.3%
3.7 1
 
0.1%
3.75 1
 
0.1%
3.8 1
 
0.1%
3.85 3
 
0.4%
3.9 2
 
0.3%
3.95 9
 
1.1%
4 46
5.8%
4.05 39
4.9%
ValueCountFrequency (%)
4.75 37
4.6%
4.7 37
4.6%
4.65 36
4.5%
4.6 45
5.6%
4.55 40
5.0%
4.5 42
5.3%
4.45 37
4.6%
4.4 54
6.8%
4.35 47
5.9%
4.3 75
9.4%

thickness
Real number (ℝ)

ZEROS 

Distinct111
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.523308
Minimum0
Maximum376.17
Zeros185
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:02.763892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112.9125
median18.4
Q320
95-th percentile26.9
Maximum376.17
Range376.17
Interquartile range (IQR)7.0875

Descriptive statistics

Standard deviation37.079864
Coefficient of variation (CV)1.8067196
Kurtosis38.892242
Mean20.523308
Median Absolute Deviation (MAD)3.25
Skewness6.0657645
Sum16377.6
Variance1374.9163
MonotonicityNot monotonic
2024-05-01T03:01:02.954606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 185
23.2%
19.9 97
 
12.2%
17.9 78
 
9.8%
18.9 28
 
3.5%
24.9 17
 
2.1%
19 17
 
2.1%
23.5 16
 
2.0%
17 16
 
2.0%
21.7 15
 
1.9%
26.9 13
 
1.6%
Other values (101) 316
39.6%
ValueCountFrequency (%)
0 185
23.2%
1.95 1
 
0.1%
2.6 4
 
0.5%
9 1
 
0.1%
10.9 4
 
0.5%
11.3 1
 
0.1%
11.5 1
 
0.1%
11.65 1
 
0.1%
12.8 1
 
0.1%
12.9 1
 
0.1%
ValueCountFrequency (%)
376.17 1
 
0.1%
277.33 1
 
0.1%
263.8 1
 
0.1%
259.4 1
 
0.1%
259 4
0.5%
249.1 2
0.3%
242 3
0.4%
240 1
 
0.1%
234.3 3
0.4%
211.1 1
 
0.1%

weight
Real number (ℝ)

MISSING 

Distinct112
Distinct (%)15.3%
Missing67
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1.8212038
Minimum0.794
Maximum3.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:03.154978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.794
5-th percentile1.25
Q11.55
median1.73
Q32.1
95-th percentile2.6
Maximum3.86
Range3.066
Interquartile range (IQR)0.55

Descriptive statistics

Standard deviation0.42158365
Coefficient of variation (CV)0.23148625
Kurtosis0.79090984
Mean1.8212038
Median Absolute Deviation (MAD)0.23
Skewness0.78386031
Sum1331.3
Variance0.17773277
MonotonicityNot monotonic
2024-05-01T03:01:03.326864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8 55
 
6.9%
1.7 54
 
6.8%
2.25 33
 
4.1%
1.86 26
 
3.3%
1.4 26
 
3.3%
1.5 25
 
3.1%
1.69 24
 
3.0%
1.6 21
 
2.6%
1.65 20
 
2.5%
1.75 17
 
2.1%
Other values (102) 430
53.9%
(Missing) 67
 
8.4%
ValueCountFrequency (%)
0.794 1
 
0.1%
0.868 1
 
0.1%
0.878 1
 
0.1%
0.908 2
0.3%
0.99 2
0.3%
0.998 2
0.3%
0.999 2
0.3%
1 3
0.4%
1.16 1
 
0.1%
1.18 1
 
0.1%
ValueCountFrequency (%)
3.86 1
 
0.1%
3.3 1
 
0.1%
3.25 1
 
0.1%
3.23 2
 
0.3%
3 2
 
0.3%
2.87 2
 
0.3%
2.8 4
0.5%
2.77 1
 
0.1%
2.72 4
0.5%
2.7 7
0.9%

warranty
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing5
Missing (%)0.6%
Memory size53.0 KiB
1.0
700 
2.0
84 
3.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2379
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 700
87.7%
2.0 84
 
10.5%
3.0 9
 
1.1%
(Missing) 5
 
0.6%

Length

2024-05-01T03:01:03.514383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:03.663465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 700
88.3%
2.0 84
 
10.6%
3.0 9
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 793
33.3%
0 793
33.3%
1 700
29.4%
2 84
 
3.5%
3 9
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1586
66.7%
Other Punctuation 793
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 793
50.0%
1 700
44.1%
2 84
 
5.3%
3 9
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 793
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 793
33.3%
0 793
33.3%
1 700
29.4%
2 84
 
3.5%
3 9
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 793
33.3%
0 793
33.3%
1 700
29.4%
2 84
 
3.5%
3 9
 
0.4%

screen_size
Real number (ℝ)

Distinct20
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.191805
Minimum11.6
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:03.804050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11.6
5-th percentile14
Q114
median15.6
Q315.6
95-th percentile16.1
Maximum18
Range6.4
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.9453087
Coefficient of variation (CV)0.062224912
Kurtosis0.61726174
Mean15.191805
Median Absolute Deviation (MAD)0
Skewness-0.72980231
Sum12123.06
Variance0.89360853
MonotonicityNot monotonic
2024-05-01T03:01:03.944673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15.6 416
52.1%
14 195
24.4%
16 100
 
12.5%
13.3 21
 
2.6%
16.1 17
 
2.1%
17.3 14
 
1.8%
17 7
 
0.9%
11.6 6
 
0.8%
14.1 5
 
0.6%
13.4 4
 
0.5%
Other values (10) 13
 
1.6%
ValueCountFrequency (%)
11.6 6
 
0.8%
13 1
 
0.1%
13.3 21
 
2.6%
13.4 4
 
0.5%
13.5 1
 
0.1%
13.6 1
 
0.1%
14 195
24.4%
14.1 5
 
0.6%
14.2 2
 
0.3%
14.5 2
 
0.3%
ValueCountFrequency (%)
18 2
 
0.3%
17.3 14
 
1.8%
17 7
 
0.9%
16.2 1
 
0.1%
16.1 17
 
2.1%
16 100
 
12.5%
15.6 416
52.1%
15.56 1
 
0.1%
15.3 1
 
0.1%
15 1
 
0.1%

resolution_width
Real number (ℝ)

Distinct18
Distinct (%)2.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2040.9084
Minimum1080
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:04.101359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1080
5-th percentile1366
Q11920
median1920
Q31920
95-th percentile2880
Maximum3840
Range2760
Interquartile range (IQR)0

Descriptive statistics

Standard deviation442.0295
Coefficient of variation (CV)0.21658468
Kurtosis5.4209197
Mean2040.9084
Median Absolute Deviation (MAD)0
Skewness2.0450953
Sum1626604
Variance195390.08
MonotonicityNot monotonic
2024-05-01T03:01:04.233503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1920 616
77.2%
2560 57
 
7.1%
1366 38
 
4.8%
2880 31
 
3.9%
3840 17
 
2.1%
3200 12
 
1.5%
1600 6
 
0.8%
1080 5
 
0.6%
1200 3
 
0.4%
3456 3
 
0.4%
Other values (8) 9
 
1.1%
ValueCountFrequency (%)
1080 5
 
0.6%
1200 3
 
0.4%
1366 38
 
4.8%
1440 1
 
0.1%
1600 6
 
0.8%
1920 616
77.2%
2160 1
 
0.1%
2240 1
 
0.1%
2520 1
 
0.1%
2560 57
 
7.1%
ValueCountFrequency (%)
3840 17
 
2.1%
3480 1
 
0.1%
3456 3
 
0.4%
3200 12
 
1.5%
3120 1
 
0.1%
3072 2
 
0.3%
3024 1
 
0.1%
2880 31
3.9%
2560 57
7.1%
2520 1
 
0.1%

resolution_height
Real number (ℝ)

Distinct21
Distinct (%)2.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1216.7503
Minimum768
Maximum2560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:04.382841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum768
5-th percentile1080
Q11080
median1080
Q31200
95-th percentile1920
Maximum2560
Range1792
Interquartile range (IQR)120

Descriptive statistics

Standard deviation330.64643
Coefficient of variation (CV)0.2717455
Kurtosis4.4617572
Mean1216.7503
Median Absolute Deviation (MAD)0
Skewness2.1076996
Sum969750
Variance109327.06
MonotonicityNot monotonic
2024-05-01T03:01:04.539088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1080 516
64.7%
1200 98
 
12.3%
1600 44
 
5.5%
768 38
 
4.8%
1800 30
 
3.8%
2400 15
 
1.9%
1440 12
 
1.5%
1920 10
 
1.3%
2560 7
 
0.9%
2000 7
 
0.9%
Other values (11) 20
 
2.5%
ValueCountFrequency (%)
768 38
 
4.8%
1080 516
64.7%
1200 98
 
12.3%
1280 1
 
0.1%
1400 1
 
0.1%
1440 12
 
1.5%
1600 44
 
5.5%
1620 5
 
0.6%
1660 1
 
0.1%
1664 1
 
0.1%
ValueCountFrequency (%)
2560 7
 
0.9%
2400 15
1.9%
2234 1
 
0.1%
2160 6
 
0.8%
2080 1
 
0.1%
2000 7
 
0.9%
1964 1
 
0.1%
1920 10
 
1.3%
1864 1
 
0.1%
1800 30
3.8%

ppi
Real number (ℝ)

Distinct50
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.0589
Minimum100
Maximum323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:04.726587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile127
Q1141
median141
Q3157
95-th percentile250
Maximum323
Range223
Interquartile range (IQR)16

Descriptive statistics

Standard deviation37.956163
Coefficient of variation (CV)0.23862961
Kurtosis3.181529
Mean159.0589
Median Absolute Deviation (MAD)1
Skewness1.9033281
Sum126929
Variance1440.6703
MonotonicityNot monotonic
2024-05-01T03:01:04.933102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141 370
46.4%
157 103
 
12.9%
142 37
 
4.6%
162 33
 
4.1%
112 21
 
2.6%
283 21
 
2.6%
243 20
 
2.5%
137 19
 
2.4%
189 17
 
2.1%
100 12
 
1.5%
Other values (40) 145
 
18.2%
ValueCountFrequency (%)
100 12
 
1.5%
112 21
 
2.6%
127 8
 
1.0%
134 1
 
0.1%
135 9
 
1.1%
137 19
 
2.4%
138 3
 
0.4%
140 4
 
0.5%
141 370
46.4%
142 37
 
4.6%
ValueCountFrequency (%)
323 1
 
0.1%
290 2
 
0.3%
283 21
2.6%
280 2
 
0.3%
266 2
 
0.3%
264 1
 
0.1%
263 1
 
0.1%
255 7
 
0.9%
254 2
 
0.3%
250 2
 
0.3%

threads
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)1.2%
Missing21
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean12.468468
Minimum2
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:05.073692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median12
Q316
95-th percentile20
Maximum32
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.2520832
Coefficient of variation (CV)0.42122922
Kurtosis1.9693815
Mean12.468468
Median Absolute Deviation (MAD)4
Skewness0.63906362
Sum9688
Variance27.584378
MonotonicityNot monotonic
2024-05-01T03:01:05.198741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
12 284
35.6%
16 191
23.9%
8 143
17.9%
20 58
 
7.3%
4 48
 
6.0%
2 27
 
3.4%
32 13
 
1.6%
24 10
 
1.3%
6 3
 
0.4%
(Missing) 21
 
2.6%
ValueCountFrequency (%)
2 27
 
3.4%
4 48
 
6.0%
6 3
 
0.4%
8 143
17.9%
12 284
35.6%
16 191
23.9%
20 58
 
7.3%
24 10
 
1.3%
32 13
 
1.6%
ValueCountFrequency (%)
32 13
 
1.6%
24 10
 
1.3%
20 58
 
7.3%
16 191
23.9%
12 284
35.6%
8 143
17.9%
6 3
 
0.4%
4 48
 
6.0%
2 27
 
3.4%

ram
Real number (ℝ)

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.789474
Minimum4
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:05.323692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q18
median16
Q316
95-th percentile32
Maximum64
Range60
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.7673873
Coefficient of variation (CV)0.49076472
Kurtosis11.783063
Mean13.789474
Median Absolute Deviation (MAD)0
Skewness2.3522104
Sum11004
Variance45.79753
MonotonicityNot monotonic
2024-05-01T03:01:05.442688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
16 431
54.0%
8 298
37.3%
32 45
 
5.6%
4 20
 
2.5%
64 3
 
0.4%
12 1
 
0.1%
ValueCountFrequency (%)
4 20
 
2.5%
8 298
37.3%
12 1
 
0.1%
16 431
54.0%
32 45
 
5.6%
64 3
 
0.4%
ValueCountFrequency (%)
64 3
 
0.4%
32 45
 
5.6%
16 431
54.0%
12 1
 
0.1%
8 298
37.3%
4 20
 
2.5%

antiglare
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
1
671 
0
127 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

Length

2024-05-01T03:01:05.602300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:05.766839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

Most occurring characters

ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

aspect_ratio
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size52.7 KiB
0
403 
16:9
256 
16:10
136 
3:2
 
3

Length

Max length5
Median length1
Mean length2.6516291
Min length1

Characters and Unicode

Total characters2116
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row16:9

Common Values

ValueCountFrequency (%)
0 403
50.5%
16:9 256
32.1%
16:10 136
 
17.0%
3:2 3
 
0.4%

Length

2024-05-01T03:01:05.975224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:06.243620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 403
50.5%
16:9 256
32.1%
16:10 136
 
17.0%
3:2 3
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 539
25.5%
1 528
25.0%
: 395
18.7%
6 392
18.5%
9 256
12.1%
3 3
 
0.1%
2 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1721
81.3%
Other Punctuation 395
 
18.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 539
31.3%
1 528
30.7%
6 392
22.8%
9 256
14.9%
3 3
 
0.2%
2 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
: 395
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2116
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 539
25.5%
1 528
25.0%
: 395
18.7%
6 392
18.5%
9 256
12.1%
3 3
 
0.1%
2 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 539
25.5%
1 528
25.0%
: 395
18.7%
6 392
18.5%
9 256
12.1%
3 3
 
0.1%
2 3
 
0.1%

touch_screen
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
0
732 
1
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

Length

2024-05-01T03:01:06.414958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:06.591707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

cores
Real number (ℝ)

Distinct10
Distinct (%)1.3%
Missing4
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean8.2380353
Minimum2
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:06.739290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q16
median8
Q310
95-th percentile14
Maximum24
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.0442089
Coefficient of variation (CV)0.49091911
Kurtosis2.086057
Mean8.2380353
Median Absolute Deviation (MAD)2
Skewness0.90330937
Sum6541
Variance16.355626
MonotonicityNot monotonic
2024-05-01T03:01:06.868675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 156
19.5%
8 149
18.7%
10 145
18.2%
4 95
11.9%
12 88
11.0%
2 71
8.9%
14 63
7.9%
24 13
 
1.6%
16 11
 
1.4%
5 3
 
0.4%
(Missing) 4
 
0.5%
ValueCountFrequency (%)
2 71
8.9%
4 95
11.9%
5 3
 
0.4%
6 156
19.5%
8 149
18.7%
10 145
18.2%
12 88
11.0%
14 63
7.9%
16 11
 
1.4%
24 13
 
1.6%
ValueCountFrequency (%)
24 13
 
1.6%
16 11
 
1.4%
14 63
7.9%
12 88
11.0%
10 145
18.2%
8 149
18.7%
6 156
19.5%
5 3
 
0.4%
4 95
11.9%
2 71
8.9%

battery_capacity
Real number (ℝ)

MISSING 

Distinct59
Distinct (%)9.9%
Missing202
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean57.002936
Minimum6
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:07.056122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile39.825
Q145
median52.5
Q368.575
95-th percentile90
Maximum100
Range94
Interquartile range (IQR)23.575

Descriptive statistics

Standard deviation17.536492
Coefficient of variation (CV)0.30764191
Kurtosis0.10937653
Mean57.002936
Median Absolute Deviation (MAD)10.5
Skewness0.80549918
Sum33973.75
Variance307.52857
MonotonicityNot monotonic
2024-05-01T03:01:07.235231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 79
 
9.9%
50 62
 
7.8%
45 38
 
4.8%
90 37
 
4.6%
70 35
 
4.4%
53.5 29
 
3.6%
52.5 28
 
3.5%
54 22
 
2.8%
42 19
 
2.4%
51 17
 
2.1%
Other values (49) 230
28.8%
(Missing) 202
25.3%
ValueCountFrequency (%)
6 1
 
0.1%
17.85 5
 
0.6%
18.5 3
 
0.4%
36 6
 
0.8%
37 7
 
0.9%
38 7
 
0.9%
39.3 1
 
0.1%
40 9
 
1.1%
41 79
9.9%
42 19
 
2.4%
ValueCountFrequency (%)
100 1
 
0.1%
99.9 6
 
0.8%
99 9
 
1.1%
97 7
 
0.9%
96 1
 
0.1%
94.3 1
 
0.1%
90 37
4.6%
87 1
 
0.1%
86 13
 
1.6%
84 1
 
0.1%

battery_cell
Categorical

MISSING 

Distinct4
Distinct (%)0.6%
Missing159
Missing (%)19.9%
Memory size53.6 KiB
3.0
413 
4.0
154 
6.0
44 
2.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1917
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 413
51.8%
4.0 154
 
19.3%
6.0 44
 
5.5%
2.0 28
 
3.5%
(Missing) 159
 
19.9%

Length

2024-05-01T03:01:07.417269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:07.589099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 413
64.6%
4.0 154
 
24.1%
6.0 44
 
6.9%
2.0 28
 
4.4%

Most occurring characters

ValueCountFrequency (%)
. 639
33.3%
0 639
33.3%
3 413
21.5%
4 154
 
8.0%
6 44
 
2.3%
2 28
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1278
66.7%
Other Punctuation 639
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 639
50.0%
3 413
32.3%
4 154
 
12.1%
6 44
 
3.4%
2 28
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 639
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1917
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 639
33.3%
0 639
33.3%
3 413
21.5%
4 154
 
8.0%
6 44
 
2.3%
2 28
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1917
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 639
33.3%
0 639
33.3%
3 413
21.5%
4 154
 
8.0%
6 44
 
2.3%
2 28
 
1.5%

hdmi
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
1
753 
0
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

Length

2024-05-01T03:01:07.729775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:07.870400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

Most occurring characters

ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

ethernet
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
0
456 
1
342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

Length

2024-05-01T03:01:07.995360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:08.152946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

Most occurring characters

ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

multi_card_reader
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
0
558 
1
240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

Length

2024-05-01T03:01:08.293571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:08.449811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

Most occurring characters

ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

thunderbolt
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
0
579 
1
219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%

Length

2024-05-01T03:01:08.574855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:08.731061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%

Most occurring characters

ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%

display_port
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
0
783 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

Length

2024-05-01T03:01:08.879495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:09.053247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

vga
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
0
796 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 796
99.7%
1 2
 
0.3%

Length

2024-05-01T03:01:09.190319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:09.330994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 796
99.7%
1 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 796
99.7%
1 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 796
99.7%
1 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 796
99.7%
1 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 796
99.7%
1 2
 
0.3%

backlit
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
1
640 
0
158 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

Length

2024-05-01T03:01:09.482919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:09.631056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

Most occurring characters

ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

fingerprint_sensor
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
0
552 
1
246 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

Length

2024-05-01T03:01:09.789661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:09.940926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

Most occurring characters

ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

inbuilt_microphone
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
1
788 
0
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

Length

2024-05-01T03:01:10.081552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:10.404167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

usb2
Categorical

MISSING 

Distinct2
Distinct (%)0.8%
Missing561
Missing (%)70.3%
Memory size55.2 KiB
1.0
195 
2.0
42 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters711
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 195
 
24.4%
2.0 42
 
5.3%
(Missing) 561
70.3%

Length

2024-05-01T03:01:10.529168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:10.669739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 195
82.3%
2.0 42
 
17.7%

Most occurring characters

ValueCountFrequency (%)
. 237
33.3%
0 237
33.3%
1 195
27.4%
2 42
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 474
66.7%
Other Punctuation 237
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 237
50.0%
1 195
41.1%
2 42
 
8.9%
Other Punctuation
ValueCountFrequency (%)
. 237
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 711
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 237
33.3%
0 237
33.3%
1 195
27.4%
2 42
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 237
33.3%
0 237
33.3%
1 195
27.4%
2 42
 
5.9%

usb3
Categorical

MISSING 

Distinct4
Distinct (%)0.5%
Missing50
Missing (%)6.3%
Memory size53.2 KiB
2.0
454 
1.0
181 
3.0
111 
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2244
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 454
56.9%
1.0 181
 
22.7%
3.0 111
 
13.9%
4.0 2
 
0.3%
(Missing) 50
 
6.3%

Length

2024-05-01T03:01:10.810413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:10.968896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 454
60.7%
1.0 181
 
24.2%
3.0 111
 
14.8%
4.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 748
33.3%
0 748
33.3%
2 454
20.2%
1 181
 
8.1%
3 111
 
4.9%
4 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1496
66.7%
Other Punctuation 748
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 748
50.0%
2 454
30.3%
1 181
 
12.1%
3 111
 
7.4%
4 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 748
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 748
33.3%
0 748
33.3%
2 454
20.2%
1 181
 
8.1%
3 111
 
4.9%
4 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 748
33.3%
0 748
33.3%
2 454
20.2%
1 181
 
8.1%
3 111
 
4.9%
4 2
 
0.1%

typec
Categorical

MISSING 

Distinct4
Distinct (%)0.5%
Missing47
Missing (%)5.9%
Memory size53.2 KiB
1.0
503 
2.0
226 
3.0
 
19
4.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2253
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 503
63.0%
2.0 226
28.3%
3.0 19
 
2.4%
4.0 3
 
0.4%
(Missing) 47
 
5.9%

Length

2024-05-01T03:01:11.125149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:11.297024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 503
67.0%
2.0 226
30.1%
3.0 19
 
2.5%
4.0 3
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 751
33.3%
0 751
33.3%
1 503
22.3%
2 226
 
10.0%
3 19
 
0.8%
4 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1502
66.7%
Other Punctuation 751
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 751
50.0%
1 503
33.5%
2 226
 
15.0%
3 19
 
1.3%
4 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 751
33.3%
0 751
33.3%
1 503
22.3%
2 226
 
10.0%
3 19
 
0.8%
4 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 751
33.3%
0 751
33.3%
1 503
22.3%
2 226
 
10.0%
3 19
 
0.8%
4 3
 
0.1%

processor_gen
Real number (ℝ)

MISSING 

Distinct11
Distinct (%)1.5%
Missing43
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean10.429139
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:11.424257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q17
median12
Q313
95-th percentile13
Maximum13
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.9480378
Coefficient of variation (CV)0.28267317
Kurtosis-0.4609637
Mean10.429139
Median Absolute Deviation (MAD)1
Skewness-1.0133317
Sum7874
Variance8.690927
MonotonicityNot monotonic
2024-05-01T03:01:11.564922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
12 215
26.9%
13 212
26.6%
11 109
13.7%
7 97
12.2%
5 75
 
9.4%
6 14
 
1.8%
3 14
 
1.8%
10 11
 
1.4%
4 4
 
0.5%
8 3
 
0.4%
(Missing) 43
 
5.4%
ValueCountFrequency (%)
3 14
 
1.8%
4 4
 
0.5%
5 75
 
9.4%
6 14
 
1.8%
7 97
12.2%
8 3
 
0.4%
9 1
 
0.1%
10 11
 
1.4%
11 109
13.7%
12 215
26.9%
ValueCountFrequency (%)
13 212
26.6%
12 215
26.9%
11 109
13.7%
10 11
 
1.4%
9 1
 
0.1%
8 3
 
0.4%
7 97
12.2%
6 14
 
1.8%
5 75
 
9.4%
4 4
 
0.5%

processor_brand
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size54.2 KiB
intel
582 
amd
206 
apple
 
8
mediatek
 
2

Length

Max length8
Median length5
Mean length4.4912281
Min length3

Characters and Unicode

Total characters3584
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowintel
2nd rowintel
3rd rowintel
4th rowintel
5th rowamd

Common Values

ValueCountFrequency (%)
intel 582
72.9%
amd 206
 
25.8%
apple 8
 
1.0%
mediatek 2
 
0.3%

Length

2024-05-01T03:01:11.705506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:11.878718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
intel 582
72.9%
amd 206
 
25.8%
apple 8
 
1.0%
mediatek 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 594
16.6%
l 590
16.5%
i 584
16.3%
t 584
16.3%
n 582
16.2%
a 216
 
6.0%
m 208
 
5.8%
d 208
 
5.8%
p 16
 
0.4%
k 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3584
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 594
16.6%
l 590
16.5%
i 584
16.3%
t 584
16.3%
n 582
16.2%
a 216
 
6.0%
m 208
 
5.8%
d 208
 
5.8%
p 16
 
0.4%
k 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 3584
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 594
16.6%
l 590
16.5%
i 584
16.3%
t 584
16.3%
n 582
16.2%
a 216
 
6.0%
m 208
 
5.8%
d 208
 
5.8%
p 16
 
0.4%
k 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 594
16.6%
l 590
16.5%
i 584
16.3%
t 584
16.3%
n 582
16.2%
a 216
 
6.0%
m 208
 
5.8%
d 208
 
5.8%
p 16
 
0.4%
k 2
 
0.1%

processor_model
Categorical

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size52.0 KiB
i5
276 
i7
144 
5
112 
i3
98 
3
78 
Other values (4)
90 

Length

Max length2
Median length2
Mean length1.7067669
Min length1

Characters and Unicode

Total characters1362
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowi3
2nd rowi5
3rd rowi5
4th rowi5
5th row5

Common Values

ValueCountFrequency (%)
i5 276
34.6%
i7 144
18.0%
5 112
14.0%
i3 98
 
12.3%
3 78
 
9.8%
7 44
 
5.5%
i9 38
 
4.8%
M2 6
 
0.8%
M1 2
 
0.3%

Length

2024-05-01T03:01:12.019361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:12.222477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
i5 276
34.6%
i7 144
18.0%
5 112
14.0%
i3 98
 
12.3%
3 78
 
9.8%
7 44
 
5.5%
i9 38
 
4.8%
m2 6
 
0.8%
m1 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i 556
40.8%
5 388
28.5%
7 188
 
13.8%
3 176
 
12.9%
9 38
 
2.8%
M 8
 
0.6%
2 6
 
0.4%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
58.6%
Lowercase Letter 556
40.8%
Uppercase Letter 8
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 388
48.6%
7 188
23.6%
3 176
22.1%
9 38
 
4.8%
2 6
 
0.8%
1 2
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
i 556
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 798
58.6%
Latin 564
41.4%

Most frequent character per script

Common
ValueCountFrequency (%)
5 388
48.6%
7 188
23.6%
3 176
22.1%
9 38
 
4.8%
2 6
 
0.8%
1 2
 
0.3%
Latin
ValueCountFrequency (%)
i 556
98.6%
M 8
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1362
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 556
40.8%
5 388
28.5%
7 188
 
13.8%
3 176
 
12.9%
9 38
 
2.8%
M 8
 
0.6%
2 6
 
0.4%
1 2
 
0.1%

graphics_brand
Categorical

Distinct5
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Memory size54.6 KiB
intel
351 
nvidia
308 
amd
128 
apple
 
8
arm
 
2

Length

Max length6
Median length5
Mean length5.0602258
Min length3

Characters and Unicode

Total characters4033
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowintel
2nd rowintel
3rd rowintel
4th rownvidia
5th rowintel

Common Values

ValueCountFrequency (%)
intel 351
44.0%
nvidia 308
38.6%
amd 128
 
16.0%
apple 8
 
1.0%
arm 2
 
0.3%
(Missing) 1
 
0.1%

Length

2024-05-01T03:01:12.432044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:12.599814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
intel 351
44.0%
nvidia 308
38.6%
amd 128
 
16.1%
apple 8
 
1.0%
arm 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i 967
24.0%
n 659
16.3%
a 446
11.1%
d 436
10.8%
e 359
 
8.9%
l 359
 
8.9%
t 351
 
8.7%
v 308
 
7.6%
m 130
 
3.2%
p 16
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4033
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 967
24.0%
n 659
16.3%
a 446
11.1%
d 436
10.8%
e 359
 
8.9%
l 359
 
8.9%
t 351
 
8.7%
v 308
 
7.6%
m 130
 
3.2%
p 16
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4033
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 967
24.0%
n 659
16.3%
a 446
11.1%
d 436
10.8%
e 359
 
8.9%
l 359
 
8.9%
t 351
 
8.7%
v 308
 
7.6%
m 130
 
3.2%
p 16
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 967
24.0%
n 659
16.3%
a 446
11.1%
d 436
10.8%
e 359
 
8.9%
l 359
 
8.9%
t 351
 
8.7%
v 308
 
7.6%
m 130
 
3.2%
p 16
 
0.4%

graphics_capacity
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)2.2%
Missing478
Missing (%)59.9%
Infinite0
Infinite (%)0.0%
Mean5.84375
Minimum2
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:12.740378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median4
Q38
95-th percentile12
Maximum16
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6665156
Coefficient of variation (CV)0.45630214
Kurtosis4.1934043
Mean5.84375
Median Absolute Deviation (MAD)2
Skewness1.8209292
Sum1870
Variance7.1103056
MonotonicityNot monotonic
2024-05-01T03:01:12.865380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 154
 
19.3%
6 69
 
8.6%
8 69
 
8.6%
16 9
 
1.1%
12 9
 
1.1%
2 8
 
1.0%
10 2
 
0.3%
(Missing) 478
59.9%
ValueCountFrequency (%)
2 8
 
1.0%
4 154
19.3%
6 69
8.6%
8 69
8.6%
10 2
 
0.3%
12 9
 
1.1%
16 9
 
1.1%
ValueCountFrequency (%)
16 9
 
1.1%
12 9
 
1.1%
10 2
 
0.3%
8 69
8.6%
6 69
8.6%
4 154
19.3%
2 8
 
1.0%

graphics_model
Categorical

IMBALANCE 

Distinct24
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size57.5 KiB
Integrated
490 
rtx3050
86 
rtx4050
 
44
rtx4060
 
43
rtx2050
 
37
Other values (19)
98 

Length

Max length10
Median length10
Mean length8.8095238
Min length4

Characters and Unicode

Total characters7030
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)1.0%

Sample

1st rowIntegrated
2nd rowIntegrated
3rd rowIntegrated
4th rowrtx4050
5th rowIntegrated

Common Values

ValueCountFrequency (%)
Integrated 490
61.4%
rtx3050 86
 
10.8%
rtx4050 44
 
5.5%
rtx4060 43
 
5.4%
rtx2050 37
 
4.6%
gtx1650 24
 
3.0%
rtx4070 19
 
2.4%
rtx4080 7
 
0.9%
rx6500m 7
 
0.9%
rtx4090 7
 
0.9%
Other values (14) 34
 
4.3%

Length

2024-05-01T03:01:13.039626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
integrated 490
61.4%
rtx3050 86
 
10.8%
rtx4050 44
 
5.5%
rtx4060 43
 
5.4%
rtx2050 37
 
4.6%
gtx1650 24
 
3.0%
rtx4070 19
 
2.4%
rtx4080 7
 
0.9%
rx6500m 7
 
0.9%
rtx4090 7
 
0.9%
Other values (14) 34
 
4.3%

Most occurring characters

ValueCountFrequency (%)
t 1278
18.2%
e 980
13.9%
r 757
10.8%
0 581
8.3%
g 515
7.3%
I 490
 
7.0%
a 490
 
7.0%
d 490
 
7.0%
n 490
 
7.0%
x 300
 
4.3%
Other values (12) 659
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5324
75.7%
Decimal Number 1216
 
17.3%
Uppercase Letter 490
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1278
24.0%
e 980
18.4%
r 757
14.2%
g 515
9.7%
a 490
 
9.2%
d 490
 
9.2%
n 490
 
9.2%
x 300
 
5.6%
m 16
 
0.3%
i 7
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 581
47.8%
5 215
 
17.7%
4 125
 
10.3%
3 101
 
8.3%
6 88
 
7.2%
2 39
 
3.2%
7 26
 
2.1%
1 25
 
2.1%
8 9
 
0.7%
9 7
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
I 490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5814
82.7%
Common 1216
 
17.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1278
22.0%
e 980
16.9%
r 757
13.0%
g 515
8.9%
I 490
 
8.4%
a 490
 
8.4%
d 490
 
8.4%
n 490
 
8.4%
x 300
 
5.2%
m 16
 
0.3%
Other values (2) 8
 
0.1%
Common
ValueCountFrequency (%)
0 581
47.8%
5 215
 
17.7%
4 125
 
10.3%
3 101
 
8.3%
6 88
 
7.2%
2 39
 
3.2%
7 26
 
2.1%
1 25
 
2.1%
8 9
 
0.7%
9 7
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1278
18.2%
e 980
13.9%
r 757
10.8%
0 581
8.3%
g 515
7.3%
I 490
 
7.0%
a 490
 
7.0%
d 490
 
7.0%
n 490
 
7.0%
x 300
 
4.3%
Other values (12) 659
9.4%

everyday_use
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
0
591 
1
207 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 591
74.1%
1 207
 
25.9%

Length

2024-05-01T03:01:13.180251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:13.336459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 591
74.1%
1 207
 
25.9%

Most occurring characters

ValueCountFrequency (%)
0 591
74.1%
1 207
 
25.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 591
74.1%
1 207
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 591
74.1%
1 207
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 591
74.1%
1 207
 
25.9%

business
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
0
739 
1
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 739
92.6%
1 59
 
7.4%

Length

2024-05-01T03:01:13.461460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:13.620074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 739
92.6%
1 59
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 739
92.6%
1 59
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 739
92.6%
1 59
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 739
92.6%
1 59
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 739
92.6%
1 59
 
7.4%

performance
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
0
446 
1
352 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 446
55.9%
1 352
44.1%

Length

2024-05-01T03:01:13.729408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:13.906710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 446
55.9%
1 352
44.1%

Most occurring characters

ValueCountFrequency (%)
0 446
55.9%
1 352
44.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 446
55.9%
1 352
44.1%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 446
55.9%
1 352
44.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 446
55.9%
1 352
44.1%

gaming
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
0
625 
1
173 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 625
78.3%
1 173
 
21.7%

Length

2024-05-01T03:01:14.034847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:14.175467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 625
78.3%
1 173
 
21.7%

Most occurring characters

ValueCountFrequency (%)
0 625
78.3%
1 173
 
21.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 625
78.3%
1 173
 
21.7%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 625
78.3%
1 173
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 625
78.3%
1 173
 
21.7%

hdd
Categorical

MISSING 

Distinct4
Distinct (%)21.1%
Missing779
Missing (%)97.6%
Memory size56.1 KiB
1024.0
15 
64.0
2 
128.0
 
1
32.0
 
1

Length

Max length6
Median length6
Mean length5.6315789
Min length4

Characters and Unicode

Total characters107
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)10.5%

Sample

1st row1024.0
2nd row1024.0
3rd row64.0
4th row1024.0
5th row64.0

Common Values

ValueCountFrequency (%)
1024.0 15
 
1.9%
64.0 2
 
0.3%
128.0 1
 
0.1%
32.0 1
 
0.1%
(Missing) 779
97.6%

Length

2024-05-01T03:01:14.331665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T03:01:14.521548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1024.0 15
78.9%
64.0 2
 
10.5%
128.0 1
 
5.3%
32.0 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 34
31.8%
. 19
17.8%
2 17
15.9%
4 17
15.9%
1 16
15.0%
6 2
 
1.9%
8 1
 
0.9%
3 1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
82.2%
Other Punctuation 19
 
17.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34
38.6%
2 17
19.3%
4 17
19.3%
1 16
18.2%
6 2
 
2.3%
8 1
 
1.1%
3 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34
31.8%
. 19
17.8%
2 17
15.9%
4 17
15.9%
1 16
15.0%
6 2
 
1.9%
8 1
 
0.9%
3 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34
31.8%
. 19
17.8%
2 17
15.9%
4 17
15.9%
1 16
15.0%
6 2
 
1.9%
8 1
 
0.9%
3 1
 
0.9%

ssd
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean608.88221
Minimum0
Maximum4096
Zeros19
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-05-01T03:01:14.646592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile256
Q1512
median512
Q3512
95-th percentile1024
Maximum4096
Range4096
Interquartile range (IQR)0

Descriptive statistics

Standard deviation315.16425
Coefficient of variation (CV)0.5176112
Kurtosis22.519518
Mean608.88221
Median Absolute Deviation (MAD)0
Skewness3.0533604
Sum485888
Variance99328.506
MonotonicityNot monotonic
2024-05-01T03:01:14.771599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
512 565
70.8%
1024 158
 
19.8%
256 37
 
4.6%
0 19
 
2.4%
2048 10
 
1.3%
128 4
 
0.5%
64 4
 
0.5%
4096 1
 
0.1%
ValueCountFrequency (%)
0 19
 
2.4%
64 4
 
0.5%
128 4
 
0.5%
256 37
 
4.6%
512 565
70.8%
1024 158
 
19.8%
2048 10
 
1.3%
4096 1
 
0.1%
ValueCountFrequency (%)
4096 1
 
0.1%
2048 10
 
1.3%
1024 158
 
19.8%
512 565
70.8%
256 37
 
4.6%
128 4
 
0.5%
64 4
 
0.5%
0 19
 
2.4%

Interactions

2024-05-01T03:00:55.861633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:15.496825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:18.320720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:21.189097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:23.889374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:26.932246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:29.908390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:32.474920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:35.402714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:38.476196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:41.145837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:43.590850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:46.031760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:48.495559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:50.828541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:53.428479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:56.011545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:15.664405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:18.504884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:21.353215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:24.092280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:27.097913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:30.093574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:32.631420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:35.584804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:38.652278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:41.314622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:43.746957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:46.181454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:48.626472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:50.977663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:53.563056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:56.170360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:15.840785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:18.690027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:21.524256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:24.309703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:27.311703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:30.278177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:32.799482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:35.775939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:38.813519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:41.478776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:43.908159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:46.331536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:48.793755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:51.151068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:53.712160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:56.329581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:16.036150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:18.912455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:21.722474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:24.498229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:27.490478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:30.432720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:33.019451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:35.980155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:38.993793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:41.629879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:44.064283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:46.498453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:48.926598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:51.307252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:53.862169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:56.479007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:16.258191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:19.140052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:21.903497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:24.706580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:27.717978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:30.616149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:33.213918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:36.193991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:39.180143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:41.787926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:44.229873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:46.655951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:49.099109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:51.459023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:54.018510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:56.641657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:16.436013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:19.295494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:22.119638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:24.907752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:27.923898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:30.771645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:33.416111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:36.376257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:39.333039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:41.940088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:44.380859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:46.807735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:49.244305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:51.611156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:54.145576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:56.803681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:16.607400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:19.462268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:22.275849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:25.127544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:28.090033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:30.941676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:33.604277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:36.594134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:39.488004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:42.107634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:44.539231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:46.965885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:49.392422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:51.772273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:54.280007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:56.945855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:16.763457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:19.628950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:22.432099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:25.322334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:28.271796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:31.097712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:33.775610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:36.819156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:39.640859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:42.267307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:44.697669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:47.123968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:49.526881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:52.100909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-05-01T03:00:57.112620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:16.937869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:19.812243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:22.603979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:25.508191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:28.440252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:31.250897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:33.951270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:36.997852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:39.803643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:42.423693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:44.862131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:47.282273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:49.701468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:52.243681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:54.636660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:57.259548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:17.234129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:19.970093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:22.760229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:25.710700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:28.590104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:31.419609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:34.115227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:37.153688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:39.945655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:42.573696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:44.993575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:47.423951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:49.827038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:52.404982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:54.793795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:57.396032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:17.395392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:20.130107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:22.916475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:25.923843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:28.931280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:31.569333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:34.268817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:37.328077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:40.097401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:42.726516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:45.142861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:47.573406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:49.977218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:52.551177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:54.930382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:57.555792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:17.560317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:20.300494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:23.067116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:26.089361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:29.092128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:31.717672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:34.470130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:37.555906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:40.250035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:42.878773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:45.309761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:47.715832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:50.133710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:52.704234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:55.081353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:57.697384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:17.707405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:20.459409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:23.233801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:26.255219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:29.256211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:31.868843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:34.810272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:37.761427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:40.389418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:43.025122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:45.452540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:47.889669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:50.266988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:52.845039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:55.246064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:57.830628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:17.845860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:20.619805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:23.374426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:26.443028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:29.415567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:32.018915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:34.960076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:37.948386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:40.537044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:43.164182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:45.597867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:48.047408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:50.407430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:52.978560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:55.396283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:57.986982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:18.003642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:20.821434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:23.515049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:26.611873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:29.551122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:32.175106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:35.103553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:38.128040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:40.673993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:43.291698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:45.726255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:48.182648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:50.542951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:53.127526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:55.562896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:58.130607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:18.164417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:21.023118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:23.670247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:26.781946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:29.707541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:32.324003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:35.265418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:38.313409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:41.012275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:43.440849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:45.891818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:48.350472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:50.701753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:53.285865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-01T03:00:55.712991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-05-01T03:00:58.429891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-01T03:00:59.360648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-01T03:00:59.951935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

laptop_modelbrandpricenum_votesratingsthicknessweightwarrantyscreen_sizeresolution_widthresolution_heightppithreadsramantiglareaspect_ratiotouch_screencoresbattery_capacitybattery_cellhdmiethernetmulti_card_readerthunderboltdisplay_portvgabacklitfingerprint_sensorinbuilt_microphoneusb2usb3typecprocessor_genprocessor_brandprocessor_modelgraphics_brandgraphics_capacitygraphics_modeleveryday_usebusinessperformancegaminghddssd
0Acer One 14 Z8-415 Laptop (11th Gen Core i3 / 8GB/ 512GB SSD/ Win11 Home)Acer259901086.04.1021.601.501.014.01920.01080.01574.081002.045.502.01100000011.03.01.011.0inteli3intelNaNIntegrated1000NaN512.0
1Wings Nuvobook V1 Laptop (11th Gen Core i5/ 8GB/ 512GB SSD/ Win11)Wings3499069.04.700.001.601.015.61080.01920.01418.081004.017.85NaN1000001011.02.01.011.0inteli5intelNaNIntegrated0100NaN512.0
2MSI Thin GF63 12HW-012IN Gaming Laptop (12th Gen Core i5/ 16GB/ 512GB SSD/ Win11 Home/ 4GB Graphics)MSI49990172.04.2521.701.862.015.61920.01080.014112.0161008.051.003.0110100101NaN3.01.012.0inteli5intel4.0Integrated0010NaN512.0
3Acer Nitro V ANV15-51 Gaming Laptop (13th Gen Core i5/ 8GB/ 512GB SSD/ Win11/ 6GB Graph)Acer7974575.04.5025.902.601.015.61920.01080.014112.081008.0NaNNaN110000101NaN3.01.013.0inteli5nvidia6.0rtx40500001NaN512.0
4Acer Aspire Lite AL15-51 Laptop (AMD Ryzen 5 5500U/ 16GB/ 512GB SSD/ Win11)Acer35990162.04.0019.701.591.015.61920.01080.014112.016116:906.036.003.01110000011.02.01.05.0amd5intelNaNIntegrated0010NaN512.0
5HP Victus 16-s0094AX Gaming Laptop (AMD Ryzen 7 7840HS/ 16GB/ 1TB SSD/ Win11/ 6GB Graph)HP92221406.04.3024.002.481.016.11920.01080.013716.016116:908.070.004.0110100101NaN3.01.07.0amd7nvidia6.0rtx30500001NaN1024.0
6Wings Nuvobook Pro Laptop (11th Gen Core i7/ 16GB/ 512GB SSD/ Win11)Wings4599089.04.1516.301.481.014.01080.01920.01578.0161004.017.85NaN1000001011.02.02.011.0inteli7intelNaNIntegrated0100NaN512.0
7HP 15s-fr2515TU Laptop (11th Gen Core i3/ 8GB/ 512GB SSD/ Win11)HP37650392.04.1017.901.701.015.61920.01080.01414.08116:902.041.003.0101000001NaN2.01.011.0inteli3intelNaNIntegrated1000NaN512.0
8Asus Vivobook 16X 2022 M1603QA-MB502WS Laptop (Ryzen 5-5600H/ 8GB/ 512GB SSD/ Win11 Home)Asus49990768.04.2520.001.801.016.01920.01200.028312.08116:1006.050.003.01000001111.02.01.05.0amd5amdNaNIntegrated0001NaN512.0
9MSI Modern 14 C11M-031IN Laptop (11th Gen Core i3/ 8GB/ 512GB SSD/ Win11 Home)MSI28990703.04.2019.351.401.014.01920.01080.01574.081002.0NaN3.01000001012.01.01.011.0inteli3intelNaNIntegrated0010NaN512.0
laptop_modelbrandpricenum_votesratingsthicknessweightwarrantyscreen_sizeresolution_widthresolution_heightppithreadsramantiglareaspect_ratiotouch_screencoresbattery_capacitybattery_cellhdmiethernetmulti_card_readerthunderboltdisplay_portvgabacklitfingerprint_sensorinbuilt_microphoneusb2usb3typecprocessor_genprocessor_brandprocessor_modelgraphics_brandgraphics_capacitygraphics_modeleveryday_usebusinessperformancegaminghddssd
1010Asus VivoBook 14 2023 X1404VA-NK541WS Laptop (13th Gen Core i5/ 16GB/ 512GB SSD/ Win11 Home)Asus6399085.04.300.01.401.014.01920.01080.015712.016116:9010.0NaN3.01000000111.02.01.013.0inteli5intelNaNIntegrated0010NaN512.0
1011Dell Inspiron 3525 D560771WIN9S Laptop (AMD Ryzen 5 5625U/ 8GB/ 512GB SSD/ Win11)Dell45999109.04.5023.51.681.015.61920.01080.014112.081006.041.03.01110001011.02.0NaN5.0amd5amdNaNIntegrated1000NaN512.0
1012Asus VivoBook 14 2023 X1404VA-NK522WS Laptop (13th Gen Core i5/ 8GB/ 512GB SSD/ Win11 Home)Asus59990130.04.200.01.401.014.01920.01080.015712.08116:9010.0NaN3.01000001111.02.01.013.0inteli5intelNaNIntegrated0010NaN512.0
1013Asus Vivobook 15 OLED 2023 X1505VA-LK542WS Laptop (13th Gen Core i5/ 16GB/ 512GB SSD/ Win11 Home)Asus7499085.04.250.01.701.015.61920.01080.014116.016116:9012.050.0NaN1000001111.02.01.013.0inteli5intelNaNIntegrated0010NaN512.0
1014Asus VivoBook 14 2023 X1404VA-NK321WS Laptop (13th Gen Core i3/ 8GB/ 512GB SSD/ Win11 Home)Asus45990148.04.550.01.401.014.01920.01080.01578.08116:906.0NaN3.01000001111.02.01.013.0inteli3intelNaNIntegrated0010NaN512.0
1015Asus Zenbook S13 OLED 2023 UX5304VA-NQ762WS Laptop (13th Gen Core i7/ 32GB/ 1TB SSD/ Win11)Asus144990100.04.3010.91.001.013.32880.01800.025512.032116:10010.063.04.0100100101NaN1.02.013.0inteli7intelNaNIntegrated0010NaN1024.0
1016Asus Zenbook 14 OLED 2023 UX3402VA-KM541WS Laptop (13th Gen Core i5/ 16GB/ 512GB SSD/ Win11 Home)Asus9299082.04.1516.91.391.014.02880.01800.024316.016116:10012.075.0NaN100000111NaN1.02.013.0inteli5intelNaNIntegrated0010NaN512.0
1017Asus Zenbook 14 OLED 2023 UX3402VA-KM742WS Laptop (13th Gen Core i7/ 16GB/ 512GB SSD/ Win11 Home)Asus11299080.04.6016.91.391.014.02880.01800.024316.016116:10012.075.0NaN100000111NaN1.02.013.0inteli7intelNaNIntegrated0010NaN512.0
1018Asus Zenbook S13 OLED 2023 UX5304VA-NQ542WS Laptop (13th Gen Core i5/ 16GB/ 512GB SSD/ Win11)Asus99990111.04.4510.91.001.013.32880.01800.025512.016116:10010.063.04.0100100101NaN1.02.013.0inteli5intelNaNIntegrated0010NaN512.0
1019Asus Vivobook S14 Flip 2022 TN3402QA-LZ520WS Laptop (AMD Ryzen 5-5600H/ 8GB/ 512GB SSD/Win11)Asus58990205.04.500.01.501.014.01920.01200.016212.08016:1016.0NaN3.01000001111.01.01.05.0amd5amdNaNIntegrated0010NaN512.0